BACKGROUND: Stroke recovery is a complex process influenced by various factors, including specific neural reorganization. The objective of this systematic review was to identify important functional connectivity (FC) changes in resting-state fMRI data that were often correlated with motor, emotional, and cognitive outcome improvement.
METHODS: A systematic search using PubMed and SCOPUS databases was conducted to identify relevant studies published between 2010 and 2023.
RESULTS: A total of 766 studies were identified, of which 20 studies (602 S individuals) met the inclusion criteria. Fourteen studies focussed on motor recovery while six on cognitive recovery. All studies reported interhemispheric FC to be strongly associated with motor and cognitive recovery. The preservation and changes of M1-M1 (eight incidences) and M1-SMA (nine incidences) FC were found to be strongly correlated with motor function improvement. For cognitive recovery, restoration and preservation of FC with and between default mode network (DMN)-related regions were important for the process.
CONCLUSIONS: This review identified specific patterns of FC that were consistently reported with recovery of motor and cognitive function. The findings may serve in refining future management strategies to enhance patient outcomes.

As an alternative to task-based functional magnetic resonance imaging (T-fMRI), resting-state functional magnetic resonance imaging (Rs-fMRI) is suggested for preoperative mapping of patients with brain tumours, with an emphasis on treatment guidance and neurodegeneration prediction. A systematic review was conducted of 18 recent studies involving 1035 patients with brain tumours and Rs-fMRI protocols. This was accomplished by searching the electronic databases PubMed, Scopus, and Web of Science. For clinical benefit, we compared Rs-fMRI to standard T-fMRI and intraoperative direct cortical stimulation (DCS). The results of Rs-fMRI and T-fMRI were compared and their correlation with intraoperative DCS results was examined through a systematic review. Our exhaustive investigation demonstrated that Rs-fMRI is a dependable and sensitive preoperative mapping technique that detects neural networks in the brain with precision and identifies crucial functional regions in agreement with intraoperative DCS. Rs-fMRI comes in handy, especially in situations where T-fMRI proves to be difficult because of patient-specific factors. Additionally, our exhaustive investigation demonstrated that Rs-fMRI is a valuable tool in the preoperative screening and evaluation of brain tumours. Furthermore, its capability to assess brain function, forecast surgical results, and enhance decision-making may render it applicable in the clinical management of brain tumours.

Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.

Major Depressive Disorder (MDD) is a psychiatric disorder characterized by functional brain deficits, as documented by resting-state functional magnetic resonance imaging (rs-fMRI) studies.
In recent years, some studies used machine learning (ML) approaches, based on rs-fMRI features, for classifying MDD from healthy controls (HC). In this context, this review aims to provide a comprehensive overview of the results of these studies.
The studies research was performed on 3 online databases, examining English-written articles published before August 5, 2022, that performed a two-class ML classification using rs-fMRI features. The search resulted in 20 eligible studies.
The reviewed studies showed good performance metrics, with better performance achieved when the dataset was restricted to a more homogeneous group in terms of disease severity. Regions within the default mode network, salience network, and central executive network were reported as the most important features in the classification algorithms.
The small sample size together with the methodological and clinical heterogeneity limited the generalizability of the findings.
In conclusion, ML applied to rs-fMRI features can be a valid approach to classify MDD and HC subjects and to discover features that can be used for additional investigation of the pathophysiology of the disease.

Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient\'s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.

BACKGROUND: Lateralized alterations in hippocampal function in the resting-state have been demonstrated for patients with temporal lobe epilepsy (TLE). However, resting-state fMRI of the hippocampus has yet to be substantiated as an adjunct to standard pre-operative assessments of the seizure focus.
OBJECTIVE: Here we report the results of a systematic review of resting-state fMRI studies investigating laterality of hippocampal network connectivity in TLE patients.
METHODS: A search of the PubMed, SCOPUS, Web of Science, and Embase databases for full-length articles written in English was conducted through June 2020 using the following terms: \'resting state fMRI,\' \'hippocampus,\' \'epilepsy,\' and \'laterality.\'
RESULTS: Our literature search yielded a total of 42 papers. After excluding studies that did not include patients with epilepsy, utilize resting-state fMRI, or explore the relationship between functional connectivity and disease lateralization, 20 publications were selected for inclusion. From these studies, a total of 528 patients, 258 with left TLE and 270 with right TLE, and 447 healthy controls were included. Of the 20 studies included, 18 found that patients with TLE demonstrated decreased hippocampal functional connectivity ipsilateral to the epileptogenic focus and 10 additionally reported increased hippocampal functional connectivity contralateral to the epileptogenic focus. Several studies demonstrated that the duration of disease was correlated with these changes in functional connectivity. This implies that a compensatory mechanism may be present in patients with treatment-refractory TLE.
CONCLUSIONS: The consistency of this hippocampal connectivity pattern across multiple studies suggests resting-state fMRI may be useful as a non-invasive diagnostic tool for preoperative evaluation of TLE patients.

One of the greatest challenges of achieving successful surgical outcomes in patients with epilepsy is the ability to properly localize the seizure onset zone (SOZ). Many techniques exist for localizing the SOZ, including intracranial electroencephalography, magnetoencephalography, and stereoelectroencephalography. Recently, resting-state functional magnetic resonance imaging (rs-fMRI) in conjunction with independent component analysis (ICA) has been utilized for presurgical planning of SOZ resection, with varying results. In this meta-analysis, we analyze the current role of rs-fMRI in identifying the SOZ for presurgical planning for patients with drug-resistant epilepsy. Specifically, we seek to demonstrate its current effectiveness compared to other methods of SOZ localization.
A literature review was conducted using the PubMed, MEDLINE, and Embase databases up to May of 2020. A total of 253 articles were screened, and seven studies were chosen for analysis. Each study was analyzed for SOZ localization by ground truth, SOZ localization by rs-fMRI with ICA, principal component analysis, or intrinsic connectivity contrast, and outcomes of surgery. A meta-analysis was performed to analyze how ground truth compares to rs-fMRI in SOZ localization.
The odds ratio comparing ground truth to rs-fMRI was 2.63 (95% confidence interval = 0.66-10.56). Average concordance of rs-fMRI SOZ localization compared with ground truth localization across studies was 71.3%.
In the hunt for less invasive presurgical planning for epilepsy surgery, rs-fMRI with ICA provides a promising avenue for future standard practice. Our preliminary results show no significant difference in surgical outcomes between traditional standards of SOZ localization and rs-fMRI with ICA. We believe that rs-fMRI could be a step forward in this search. Further investigation comparing rs-fMRI to traditional methods of SOZ localization should be conducted, with the hope of moving toward relying solely on noninvasive screening methods.